Uber’s Drivers: Information Asymmetries And Control In Dynamic Work

This empirical study explores labor in the on-demand economy using the rideshare service Uber as a case study. By conducting sustained monitoring of online driver forums and interviewing Uber drivers, we explore worker experiences within the on-demand economy. We argue that Uber’s digitally and algorithmically mediated system of flexible employment builds new forms of surveillance and control into the experience of using the system, which result in asymmetries around information and power for workers. In Uber’s system, algorithms, CSRs, passengers, semiautomated performance evaluations, and the rating system all act as a combined substitute for direct managerial control over drivers, but distributed responsibility for remote worker management also exacerbates power asymmetries between Uber and its drivers. Our study of the Uber driver experience points to the need for greater attention to the role of platform disintermediation in shaping power relations and communications between employers and workers.

Uber’s Drivers: Information Asymmetries And Control In Dynamic Work – Introduction

The Uber “rideshare” smartphone application, which connects drivers of private vehicles with riders, markets itself as part of the so-called “peer,” “sharing,” or “on-demand” economy (Scholz, 2013), characterized by workers who are independent contractors and not employees (Malhotra & Van Alstyne, 2014). Uber claims that it is “just an app” (Lowrey, 2015), an intermediary platform (Gillespie, 2010, p.2) between users, passengers and drivers which eludes legal responsibility as a traditional employer. This positioning reveals the tensions in which the company is often embroiled: Uber makes claims that its platform fosters entrepreneurship in drivers, while simultaneously exerting significant control over how drivers do their jobs through constant monitoring, predictive and real-time scheduling management, routine performance evaluations, and implicit and explicit rules about driver performance.

Our research addresses the question of how workers experience labor under this regime of algorithmic and semi-automated electronic management (Irani, 2015, p. 228). Our methods combine a qualitative study of Uber drivers in both digital and physical spaces with a design critique of Uber’s technical systems and a discursive critique of its corporate communications (ads, interviews, policies). Our findings coalesced around the dynamics of Uber’s system of surveillance and control over workers’ behavior. Our conclusions are two-fold: first, that the information asymmetries produced by Uber’s system are fundamental to its ability to structure indirect control over its workers; and second, that Uber relies heavily on the evolving rhetoric of the algorithm to justify these information asymmetries to drivers, riders, as well as regulators and outlets of public opinion. In support, this paper examines four main features of Uber’s system: electronic monitoring; surge pricing and labor scheduling; the conflation of real-time and predictive analysis; and driver ratings. In each of these cases, we posit not only the intention behind Uber’s design choices to leverage or effect control indirectly, but the emergent practices of resistance that networks of drivers have developed in response. This two-part analysis illustrates how labor under algorithmic management is not characterized by freedom and flexibility, but by opposing conditions of surveillance and resistance.

We performed archival and real-time analysis of online Uber drivers between December 2014 and September 2015.1 Online forums are particularly vital for understanding the experiential knowledge of Uber drivers. Drivers use these forums in a number of ways: to learn tricks and tips for being successful within Uber’s platform; to compare and share practices; to vent about passengers and the company; and to debate Uber’s practices, and discrepancies between the passenger and driver apps (Rosenblat, 2015; Clark, 2015; Snyder, 2015). The knowledge that drivers must acquire to be successful within Uber’s information space is shared in these semiprivate or discrete publics. Since driver contact with physical Uber managers are primarily limited to the initial recruitment process, these communication networks are of particular importance – Uber communicates with its drivers almost exclusively via email and text.

Data was observed and collected from five dedicated forums (three larger, and two smaller).2 Of these larger forums, Forum A is hosted as a standalone website and has 700-1000 daily visitors, according to the forum operator. Forum B is a closed-membership forum hosted on a social media platform and has around 5100 members (the numbers change marginally on a daily basis after increasing by hundreds over the 6-month period). Forum C is a standalone website (i.e. not hosted on a social media site) dedicated to Uber drivers: it has numerous participants and appears to be the largest, but the exact number of participants is unknown. Posters are required to enter minimal contact information in order to register and post on it; the majority of forum participants identify as drivers within United States. Approximately 1350 total archival items were collected, documenting the activities and conversations of drivers through forum posts, interviews, and other personal contacts, including email correspondence with Uber Community Support Representatives (CSRs), selected out of thousands of posts made over a nine-month period. To contextualize and extend the data gathered from forums, numerous casual conversations with Uber drivers took place during normal use of the service. Recognizing the limitations of such informal in-person conversations (e.g., drivers’ discomfort with talking about the service while on the job), researchers also conducted eight in-depth, semistructured interviews with seven individual drivers to follow-up on issues observed in online forums.

The experiences reported on forums and described by interview subjects are not necessarily representative of the Uber driver population as a whole. For instance, drivers who seek out and participate in online forums may be more strongly opinionated than other drivers, or may have had individual difficulties that drove them to seek help and information online. The ability to generalize from reported driver experiences is also complicated by the range of Uber services (of which uberX appears to be the most common) and drivers’ tendency to not identify for which tier of service they drive. However, while these accounts might not describe every driver’s experience, the collected evidence nonetheless reveals several structural features of the Uber system that could potentially affect any driver employed. This work provides a glimpse into the potentially messy work of being an Uber driver, and a starting point for formulating future research questions about the Uber driver experience.